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. 2018 Feb 22;11(5):614-630.
doi: 10.1111/eva.12591. eCollection 2018 Jun.

Large-scale genetic panmixia in the blue shark (Prionace glauca): A single worldwide population, or a genetic lag-time effect of the "grey zone" of differentiation?

Diane Bailleul  1   2 , Alicia Mackenzie  1   2 , Olivier Sacchi  1   2 , François Poisson  1 , Nicolas Bierne  2   3 , Sophie Arnaud-Haond  1   2
Affiliations

Large-scale genetic panmixia in the blue shark (Prionace glauca): A single worldwide population, or a genetic lag-time effect of the "grey zone" of differentiation?

Diane Bailleul et al. Evol Appl. .

Abstract

The blue shark Prionace glauca, among the most common and widely studied pelagic sharks, is a top predator, exhibiting the widest distribution range. However, little is known about its population structure and spatial dynamics. With an estimated removal of 10-20 million individuals per year by fisheries, the species is classified as "Near Threatened" by International Union for Conservation of Nature. We lack the knowledge to forecast the long-term consequences of such a huge removal on this top predator itself and on its trophic network. The genetic analysis of more than 200 samples collected at broad scale (from Mediterranean Sea, North Atlantic and Pacific Oceans) using mtDNA and nine microsatellite markers allowed to detect signatures of genetic bottlenecks but a nearly complete genetic homogeneity across the entire studied range. This apparent panmixia could be explained by a genetic lag-time effect illustrated by simulations of demographic changes that were not detectable through standard genetic analysis before a long transitional phase here introduced as the "population grey zone." The results presented here can thus encompass distinct explanatory scenarios spanning from a single demographic population to several independent populations. This limitation prevents the genetic-based delineation of stocks and thus the ability to anticipate the consequences of severe depletions at all scales. More information is required for the conservation of population(s) and management of stocks, which may be provided by large-scale sampling not only of individuals worldwide, but also of loci genomewide.

Keywords: Prionace glauca; blue shark; conservation; fisheries; genetic panmixia; stock.

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Figures

Figure 1
Figure 1
The “grey zone” of population differentiation. Analogy to De Queiroz speciation grey zone: inside the “grey zone”, it is impossible to discriminate populations based on genetic data alone. N, population size; m, migration rate
Figure 2
Figure 2
Sampling sites across blue shark distribution area. The distribution area is drawn in blue, and sampling sites are represented by blue dots for the Mediterranean Sea (Gulf of Lion, Malta and Greece), green for the Atlantic Ocean (Spain and Azores) and orange for the Pacific Ocean (Australia, New Zealand and Hawaii)
Figure 3
Figure 3
Haplotype network of blue shark individuals. Each circle represents a unique haplotype, and their sizes are proportional to the number of individuals sharing this haplotype. The colour inside each circle indicates the sampling site origin of the individual. The lengths of the branches joining the circles are proportional to the number of differences between the haplotypes
Figure 4
Figure 4
Power determination from mitochondrial (a) and microsatellite (b) data sets with POWSIM. Every couple of population size/number of generations corresponds to a FST value. POWSIM's results indicated the mitochondrial data set has the power to detect correctly FST values from approximately 0.01 (a) and the microsatellite data set FST values from approximately 0.0026 (b)
Figure 5
Figure 5
Bayesian clustering of blue shark individuals from STRUCTURE analysis. a: Within barplots for K from 2 to 5. Each individual is represented by a vertical bar partitioned into coloured sub‐bars whose lengths are proportional to its estimated probability of membership for the K clusters. b: Plot of the mean of estimated “log probability of data” for each value of K. c: Delta K of Evanno's method based on the rate of change in the log probability of data. d: Evanno table output for K from 1 to 5
Figure 6
Figure 6
The “grey zone” of population differentiation illustrated with data from simulations of splits among populations with increasing size N e, exchanging a variable number of migrants m. Simulated population separation process with N e = 10,000 and 100,000 and N e m = 0, 1 and 10. For each plot, the x‐axis represents the number of generations since the divergence, the right y‐axis the FST values (blue lines, full for the median value and dashed for the 95% envelope) and the left y‐axis the percentage of significant FST values (green line). The “population grey zone,” in shades of grey, indicates the number of generations since the split, during which FST computed on subsamples is likely not to be statistically supported, and thus, the number of distinct populations will remain elusive
Figure 7
Figure 7
Illustration of the impact of huge bottlenecks with data from simulations of splits among populations. Simulated population separation process with initial N e = 1,000,000 and m = 0.0001. After 500 generations (first dashed red line), each population was reduced by 90% and 99%. The detection of significant sub‐FST above 95% is indicated by the second dashed red line. For each plot, the x‐axis represents the number of generations since the divergence, the right y‐axis the FST values (blue lines, full for the median value and dashed for the 95% envelope) and the left y‐axis the percentage of significant FST values (green line)
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